| Biography | |
|---|---|
 Dr. Kim Phuc Tran the ENSAIT and the GEMTEX laboratory, University of Lille, France |
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| Title: Industrial Artificial Intelligence for Smart Manufacturing: an application in supply chain management | |
| Abstract: In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). The recent development of information and communication technologies has engendered the concept of the smart factory that adds intelligence into the manufacturing process to drive continuous improvement, knowledge transfer, and data-based decision making. SM leverages the automation of processes with less human intervention, the flexibility that allows for early system failure detection, and system automation. Supply chain management affects manufacturing in a variety of ways, including the availability of inputs needed for production processes, and costs. Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time series data and an LSTM Autoencoder network-based method combined with a one-class support vector machine algorithm for detecting anomalies in sales. Unlike other approaches, we recommend combining external and internal company data sources for the purpose of enhancing the performance of forecasting algorithms using multivariate LSTM with the optimal hyperparameters. In addition, we also propose a method to optimize hyperparameters for hybrid algorithms for detecting anomalies in time series data. The proposed approaches will be applied to both benchmarking datasets and real data in fashion retail. The obtained results show that the LSTM Autoencoder based method leads to better performance for anomaly detection compared to the LSTM based method suggested in a previous study. The proposed forecasting method for multivariate time series data also performs better than some other methods based on a dataset provided by NASA. | |
| Biography: Dr. Kim Phuc Tran is currently an Associate Professor in Automation and Industrial Informatics at the ENSAIT and the GEMTEX laboratory, University of Lille, France. He received the Engineering degree and the Master of Engineering degree in Automated Manufacturing. He obtained a Ph.D. in Automation and Applied Informatics at the Université de Nantes, Nantes, France. His research works deal with Real-time Anomaly Detection with Machine Learning, Decision support systems with Artificial Intelligence, and Enabling Smart Manufacturing with IIoT, Federated learning, and Edge computing. He has published more than 50 papers in peer-reviewed international journals and proceedings of international conferences. He is the Topic Editor and Guest Editor for Special Issue "Artificial Intelligence for Smart Manufacturing: Methods and Applications" for Sensors. In addition, as the project coordinator, he is conducting 1 regional research project about Healthcare System with Federated Learning. He has been or is involved in 3 regional research and European projects. He is an expert and evaluator for the Research and Innovation program of the Government of the French Community, Belgium. | |
